Silver Nanocubes Coated in Ceria: Core/Shell Size Effects on Light-Induced Charge Transfer
Bibliographic record
Abstract
Abstract Plasmonic sensitization of semiconductors is an attractive approach to increase light-induced photocatalytic performance; one method is to use plasmonic nanostructures in core@shell geometry. The occurrence and mechanism of synergetic effects in photocatalysis of such geometries are under intense debate and proposed to occur either through light-induced charge transfer (CT) or through thermal effects. This study focuses on the relation between the dimensions of Ag@CeO2 nanocubes, the wavelength-dependent efficiency, and the mechanism of light-induced direct CT. A 4-mercaptobenzoic acid (4-MBA) linker between core and shell acts as a Raman probe for CT. For all Ag@CeO2 nanocubes, CT increases with decreasing excitation wavelength, with notable increase at and below 514 nm. This is fully explainable by CT from silver to the 4-MBA LUMO, with the increase for excitation wavelengths that exceed the Ag/4-MBA LUMO gap of 2.28 eV (543 nm). A second general trend observed is an increase in CT yield with ceria shell thickness, which is assigned to relaxation of the excited electron further into the ceria conduction band, potentially producing defects.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".